CHILD MALNUTRITION AND SPATIAL ANALYTICS: A COMPREHENSIVE REVIEW OF LITERATURE AND RESOURCES

Abstract

Background
Child malnutrition remains a critical public health challenge, particularly in low and middle-income countries (LMICs), where it contributes to high rates of morbidity, mortality, and long-term developmental impairments. Traditional national-level statistics often obscure significant geographic disparities within countries. Spatial analytics has emerged as a transformative approach to uncovering these hidden patterns and informing more targeted and effective nutrition interventions.

Methods
This review synthesizes academic literature, including peer-reviewed articles, theses, and books, that apply spatial analysis techniques such as Geographic Information Systems (GIS), spatial regression, and cluster detection to the study of child malnutrition. It also reviews open-access data sources, including Demographic and Health Surveys (DHS), UNICEF's Multiple Indicator Cluster Surveys (MICS), and global nutrition databases, which enable the spatial mapping and analysis of malnutrition patterns.

Results
Studies from various countries, including India, Ethiopia, Bangladesh, Uganda, Peru, and the Philippines, demonstrate the effectiveness of spatial analytics in identifying malnutrition hotspots, understanding spatial clustering, and revealing regional variations in the impact of risk factors such as poverty, maternal education, sanitation, and environmental conditions. These insights support the development of geographically nuanced public health strategies and interventions.

Conclusion
The review emphasizes the need to maximize the impact of spatial analytics in addressing child malnutrition by strategically investing in data systems, strengthening capacity, and integrating spatial tools into health and nutrition programs. A multidisciplinary approach that bridges nutrition science, geography, data science, and public health is crucial for translating spatial insights into targeted, evidence-based interventions, especially in diverse and resource-constrained contexts.